جست‌وجوی استاد و پژوهشگر

• Algorithmic Game Theory
• Computational Social Choice
• Artificial Intelligence
• +۳ مورد دیگر

Edith Elkind is a Professor of Computer Science at the University of Oxford and a Non-Tutorial Fellow at Balliol College (leaving October 2024). Her research focuses on algorithmic game theory and computational social choice. She joined Oxford in 2013 after roles at Nanyang Technological University (Singapore), Princeton University (PhD 2005), and postdoctoral positions at the University of Warwick, University of Liverpool, Hebrew University of Jerusalem, and University of Southampton. Her work addresses voting systems, cooperative games, and complexity analysis. Key contributions include studies on multiwinner elections, justified representation, and coalition formation. She has advised six PhD/Master’s students and authored/co-authored over 30 publications. Notable works include Properties of multiwinner voting rules (2017) and Justified representation in approval-based committee voting (2017), advancing fairness and efficiency in voting mechanisms. Her research spans theoretical computer science and AI, with applications in social choice theory and game dynamics. Collaborations include projects on foundational AI and computational social choice. She has contributed to foundational texts like Computational Aspects of Cooperative Game Theory (2011).

• Epistemology
• Formal Epistemology
• Philosophy of Science
• +۳ مورد دیگر

William Roche is a Professor of Philosophy at Texas Christian University (TCU), affiliated with the AddRan College of Liberal Arts. He holds a Ph.D., M.S., and B.S. in Philosophy from The Ohio State University and the University of Utah. His primary research focuses on epistemology, formal epistemology, and philosophy of science, with additional interests in logic, philosophy of language, and philosophy of mind. Roche teaches courses such as Critical Reasoning, Symbolic Logic I, Introduction to Epistemology, and Advanced Issues in Epistemology. His work explores topics like foundationalism, Bayesian confirmation theory, and probabilistic support structures. Recent publications include discussions on the perils of parsimony, evidence screening-off mechanisms, and infinite regresses in epistemological frameworks. His research bridges formal methods with traditional philosophical inquiry, addressing questions in scientific methodology and epistemic justification. Roche’s contributions often engage with debates in cognitive science and the logical underpinnings of knowledge claims.

Scott Lindermanمشاهده پروفایل

استادیار

• Machine Learning
• Computational Neuroscience
• State Space Models
• +۴ مورد دیگر

Scott W. Linderman is an Assistant Professor of Statistics at Stanford University and a Faculty Scholar at the Wu Tsai Neurosciences Institute. He holds courtesy appointments in Computer Science and is affiliated with Stanford Bio-X and the Stanford AI Lab. His research focuses on developing probabilistic models and statistical methods to analyze neural data, bridging computational neuroscience and machine learning. Linderman earned his PhD in Computer Science from Harvard University, with postdoctoral training at Columbia University under Liam Paninski and David Blei. He previously worked as a software engineer at Microsoft and holds an undergraduate degree in Electrical and Computer Engineering from Cornell University. Research Interests : Machine learning, computational neuroscience, state space models, neural data analysis, and probabilistic modeling. His lab develops tools like the SSM and Dynamax packages, applying methods to problems such as neural decoding, behavioral tracking, and understanding latent neural dynamics. Awards : 2023 McKnight Scholar Award, 2022 Sloan Research Fellowship, Leonard J. Savage Award (2016). Linderman has advised over 20 PhD students and postdocs, contributing to breakthroughs in neuroscience and machine learning. His work includes collaborations with experimental neuroscientists like David Anderson and Sebastian Seung. Labs & Teams : Linderman Lab focuses on advancing statistical methods for neuroscience. Key projects include state space models (e.g., rSLDS, Gaussian Process SLDS) and behavioral analysis tools like Keypoint MoSeq. The lab emphasizes open-source software and interdisciplinary collaboration.

Keith J. Holyoakمشاهده پروفایل

استاد

• Cognitive Psychology
• Human Reasoning
• Analogy
• +۵ مورد دیگر

Keith J. Holyoak is a Distinguished Professor in the Department of Psychology at the University of California, Los Angeles (UCLA), where he conducts foundational research in cognitive psychology. His work centers on human reasoning, learning, decision making, and problem solving, with a specific focus on the psychological mechanisms of analogy and relational knowledge across diverse domains including law, politics, mathematics, and science. He directs the Reasoning Lab at UCLA, integrating experimental, computational, and neuroimaging approaches to investigate cognitive processes. Education: Ph.D. from Stanford University Research Focus: Holyoak's research program systematically explores how analogy facilitates knowledge transfer and learning, examining the neural underpinnings of complex reasoning with emphasis on prefrontal cortex functions. His work bridges theoretical cognitive science with practical applications, investigating causal learning, deductive processes, and the constraints shaping human inference. Through computational modeling and cross-domain studies, he reveals universal principles governing relational reasoning while addressing domain-specific manifestations in scientific, legal, and social contexts. Publication Trends: His scholarly output demonstrates consistent evolution from foundational work on pragmatic reasoning schemas (1980s) toward integrated models of causal learning and Bayesian inference (2000s-2010s). Recent publications emphasize rational analysis frameworks, cross-species comparisons in causal cognition, and the role of invariance principles in knowledge generalization. The corpus reveals deep methodological pluralism—combining behavioral experiments, computational modeling, and neuroimaging—to address fundamental questions about the architecture of human thought. Research Infrastructure: Holyoak leads the Reasoning Lab (https://reasoninglab.psych.ucla.edu), which maintains a collaborative environment for interdisciplinary research. The lab's infrastructure supports advanced experimental paradigms, computational modeling suites, and neurocognitive investigations, facilitating research on analogical transfer, causal inference, and decision-making under uncertainty across the lifespan.

Peng Dingمشاهده پروفایل

دانشیار

• Causal Inference
• Biostatistics
• Social Science Statistics
• +۳ مورد دیگر

Peng Ding is an Associate Professor in the Department of Statistics at the University of California, Berkeley. He holds a B.S. in Mathematics and B.A. in Economics from Peking University, followed by an M.S. in Statistics from the same institution. He earned his Ph.D. in Statistics from Harvard University in 2015 and completed a postdoctoral fellowship at Harvard T.H. Chan School of Public Health. His research focuses on causal inference, missing data, Bayesian statistics, and applied statistical methods in biomedical and social sciences. Ding is particularly known for his work on improving the robustness of causal inference in observational studies and randomized experiments through sensitivity analysis and design-based approaches. His research interests include methodologies to address contaminated data (e.g., missing values, measurement errors), factorial experiments, and sensitivity analysis for unmeasured confounding. He has contributed to theoretical advancements in rerandomization, regression adjustment, and instrumental variable techniques. His work emphasizes practical applications in fields such as epidemiology, social sciences, and public health. Peng Ding teaches courses on causal inference, statistical theory, and linear models. His most recent courses include Data, Inference, and Decisions and Linear Models . He actively mentors graduate and undergraduate students through directed study programs. His research has been published in top-tier statistical journals and presented at international conferences.

Shideh Dashtiمشاهده پروفایل

استاد

• Geotechnical Engineering
• Geomechanics
• Seismic Soil-Structure Interaction
• +۷ مورد دیگر

Shideh Dashti is a Professor and Associate Chair for Administration in Geotechnical Engineering & Geomechanics at the University of Colorado Boulder's Department of Civil, Environmental, and Architectural Engineering within the College of Engineering and Applied Sciences. She holds a PhD (2009) and MS (2005) from UC Berkeley and a BS (2004) from Cornell University where she graduated Magna cum Laude. Dr. Dashti's research focuses on the intersection of geotechnical engineering, infrastructure resilience, and environmental sustainability. Her work spans physical modeling (particularly centrifuge testing), seismic soil-structure interaction, liquefaction mechanisms in urban environments, and the impact of compound hydrologic-seismic hazards on infrastructure. She has pioneered research on the seismic response of underground structures in dense urban settings and performance-based design of liquefaction remediation techniques. Analysis of her recent publications (2021-2025) reveals a strong emphasis on liquefaction mitigation techniques, particularly using dense granular columns and ground densification. Her work increasingly incorporates machine learning approaches and examines the intersection of infrastructure resilience with social equity, as evidenced by her research on environmental vulnerability in incarceration facilities. Her publications predominantly appear in top-tier journals like the ASCE Journal of Geotechnical and GeoEnvironmental Engineering. 2025 Earthquake Engineering Research Institute (EERI) Distinguished Lecture Award 2024 Campus Sustainability Award, CU Boulder 2021 Walter L. Huber Civil Engineering Research Price, ASCE 2015 National Science Foundation Early CAREER Award 2018 Arthur Casagrande Professional Development Award, ASCE Dr. Dashti has received significant research funding including the prestigious NSF CAREER award. She serves as Co-leader and Steering Committee member of GeoEngineering Extreme Event Reconnaissance (GEER), having participated in 8 reconnaissance efforts since 2011 (leading 3). She is also an ISSMGE TC104 committee member and one of two U.S. representatives. Her professional service includes editorial roles, notably as Associate Editor of the Year for the ASCE Journal of Geotechnical Engineering and Geomechanics (2020). As a leader in the field, Dr. Dashti directs research focused on resilient infrastructure with sustainability and equity. She leads the GAANN fellowships in Integrative Reengineering of Infrastructure and is affiliated with the Center for Infrastructure, Energy, and Space Testing. Her work bridges traditional geotechnical engineering with contemporary concerns about social justice and environmental sustainability in infrastructure systems.

Zachary E. Rossمشاهده پروفایل

استاد

• Seismology
• Geophysics
• Machine Learning
• +۷ مورد دیگر

Zachary E. Ross is a Professor of Geophysics at the California Institute of Technology (Caltech) and holds the William H. Hurt Scholar distinction since 2021. His research integrates machine learning, computational mathematics, and seismology to analyze earthquakes and fault zones using large seismic datasets. Education B.S., University of California, Davis (2009) M.S., California Polytechnic State University, San Luis Obispo (2011) Ph.D., University of Southern California (2016) His research focuses on high-resolution imaging of fault zones, understanding earthquake sequences in space and time, and applying artificial intelligence to seismic data analysis. He develops scalable algorithms for waveform inversion, ground-motion synthesis, and real-time seismic monitoring. Recent publications highlight his work on neural operators for wave propagation, AI-driven seismicity analysis, and induced earthquake dynamics. He teaches advanced courses including Ge 264 – Machine Learning in Geophysics and Ge 271 – Dynamics of Seismicity . Scientific Awards William H. Hurt Scholar (2021–present)

Lutz Warnkeمشاهده پروفایل

استاد

• Combinatorics
• Probability Theory
• Random Graphs
• +۵ مورد دیگر

Lutz Warnke is a Professor of Mathematics at the University of California, San Diego, with prior affiliations at Georgia Institute of Technology (where he received tenure in 2021) and Peterhouse, Cambridge University (Junior Research Fellow until 2016). His research focuses on probabilistic combinatorics, random graphs, phase transitions, and combinatorial probability, with applications to extremal combinatorics and Ramsey theory. Education : Ph.D. in Mathematics from the University of Oxford (2012), supervised by Oliver Riordan. Dr. Warnke's research explores the structure and evolution of random graphs and processes, including Achlioptas processes, Ramsey numbers, and extremal problems. His work often bridges probabilistic methods with algorithmic applications and theoretical computer science. His publications from 2022–2025 reveal trends in random graph isomorphisms, clique coloring thresholds, extremal subgraph counts, and hardness of online algorithms. Key subfields include percolation, phase transitions, and probabilistic methods applied to combinatorial structures. Scientific Awards : Dénes König Prize (2016), Alfred P. Sloan Research Fellowship (2018), NSF CAREER Award (2020), Richard Rado Prize (2014). Dr. Warnke actively supervises PhD students and postdocs, including Matthew Cho (PhD ongoing), Erlang Surya (PhD 2025), Emily Zhu (PhD 2025), and He Guo (PhD 2021). He has received teaching accolades at Georgia Tech and contributes to graduate courses in probabilistic combinatorics, random graph theory, and stochastic processes.

Andrea Collevecchioمشاهده پروفایل

استاد

• Probability
• Mathematical Physics
• Statistical Mechanics
• +۳ مورد دیگر

Andrea Collevecchio is a Professor in the School of Mathematics at Monash University, Australia, where he has been a faculty member since 2012. His research focuses on the intersection of Probability, Mathematical Physics, and Statistical Mechanics, with particular expertise in stochastic processes and theoretical modeling. He earned his PhD in Statistics from Purdue University in 2004, followed by postdoctoral positions in Italy and Germany. In 2006, he became Assistant Professor at Ca’Foscari University in Venice before joining Monash University. Collevecchio specializes in Reinforced Processes and Large Deviations, investigating complex systems through random walk models. His work bridges abstract probability theory with applications in statistical mechanics, examining phenomena like memory effects in stochastic processes and phase transitions in lattice systems. Recent research emphasizes hypercube structures, non-reversible dynamics, and reinforcement mechanisms. His 2021-2025 publications reveal a concentrated focus on hypercube random walks, with increasing exploration of non-reversible processes, vertex-reinforced dynamics, and bootstrap methods. These works consistently apply probabilistic frameworks to problems in mathematical physics, demonstrating strong connections between theoretical probability and physical modeling. Collevecchio has secured multiple research grants including ARC-funded projects on self-interacting random walks (2023-2026) and random walks with long memory (2018-2022). He contributes to interdisciplinary initiatives like the Smart Vehicles project for dementia support (2025-2027) and actively organizes academic events including the AIM Day series connecting mathematics, AI, and industry applications.

Maryam Aliakbarpourمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Statistical Inference
• Learning Theory
• +۲ مورد دیگر

Maryam Aliakbarpour is the Michael B. Yuen and Sandra A. Tsai Assistant Professor in the Department of Computer Science at Rice University, affiliated with the Ken Kennedy Institute. She holds a Ph.D. and M.S. from MIT (2020 and 2015) and a B.S. from Sharif University of Technology (2013). Her research focuses on theoretical computer science, statistical inference, learning theory, differential privacy, and hypothesis testing, with an emphasis on algorithm design under computational and privacy constraints. She has held postdoctoral positions at Boston University, Northeastern University, and UMass Amherst, and participated in the Simons Institute's 2020 program on high-dimensional computation. Her work bridges foundational theory and practical applications, particularly in designing efficient algorithms for distribution testing, privacy-preserving machine learning, and hypothesis selection. Notable contributions include optimal algorithms for distribution testing under memory constraints and advancements in differential privacy for metalearning. She has received the Rising Stars in EECS (2018) and MIT’s Neekeyfar Award. Teaching includes graduate courses on learning theory and probabilistic methods, emphasizing algorithmic tools for modern computational challenges. Her publications span top conferences like COLT, NeurIPS, and ICML, addressing topics such as privacy-aware learning, efficient entropy estimation, and robust statistical methods. She advises on research projects requiring strong algorithmic foundations and mentors students in theoretical computer science and data privacy.

• Nonlinear analysis
• Scattering theory
• Real and complex analysis
• +۷ مورد دیگر

Ronald Coifman is the Sterling Professor of Mathematics and Professor of Computer Science at Yale University. His research focuses on nonlinear analysis, scattering theory, complex analysis, numerical analysis, and their applications in data science, signal processing, and biomedical imaging. He holds the National Medal of Science and is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. Coifman's work bridges pure mathematics and applied sciences, emphasizing harmonic analysis, manifold learning, and data-driven modeling. His contributions include foundational advancements in wavelet theory, diffusion maps, and nonlinear dimensionality reduction techniques. Key innovations include the development of empirical intrinsic geometry for analyzing complex systems and the use of Wasserstein distances in high-dimensional data analysis. His academic portfolio includes over 250 publications since the 1960s, spanning topics from theoretical mathematics to practical medical diagnostics. Notable applications include methods for stroke detection, medical imaging analysis, and anomaly detection in dynamic systems. Coifman collaborates across disciplines, integrating computational methods with domain-specific challenges in biology, chemistry, and engineering. Education: Ph.D. in Mathematics from the University of Geneva (1965) Awards: National Medal of Science (2001), Member of NAS (1993), Member of AAAS (2006) Key Projects: Development of diffusion maps, manifold learning algorithms, and empirical geometry frameworks Coifman's current research explores the intersection of machine learning and mathematical analysis, with recent focus on intrinsic data organization, emergent dynamical models, and scalable computational methods for large datasets.

Tianyu Guanمشاهده پروفایل

استادیار

• Sports Analytics
• Machine Learning
• Functional Data Analysis
• +۶ مورد دیگر

Tianyu Guan is an Assistant Professor in the Department of Mathematics and Statistics at York University, Faculty of Science. He previously served as an Assistant Professor at Brock University and joined York University in 2024. He holds a PhD in Statistics from Simon Fraser University (2020), an MSc in Actuarial Science from the same institution (2014), and a BSc in Statistics from Jilin University (2011). PhD in Statistics, Simon Fraser University, 2020 MSc in Actuarial Science, Simon Fraser University, 2014 BSc in Statistics, Jilin University, 2011 His research centers on sports analytics, functional data analysis, and nonparametric statistics, with strong applications in machine learning and data science. He applies statistical methodologies to understand sports performance, player behavior, and game dynamics. His work also extends to theoretical developments in sparse modeling and functional regression. The recent publications highlight a clear trend toward integrating advanced statistical techniques with real-world sports and entertainment data. His work combines functional data analysis, machine learning, and probabilistic modeling to extract insights from complex longitudinal and high-dimensional datasets. Topics span soccer, rugby, football, and movie reviews, demonstrating interdisciplinary reach. While no formal scientific awards are listed in the provided text, his publications in high-impact journals such as Annals of Applied Statistics and Statistics and Computing reflect strong academic recognition. Tianyu Guan actively advises multiple graduate students at both MSc and PhD levels, primarily at Brock and Simon Fraser Universities. His teaching portfolio includes advanced courses in nonparametric statistics, sampling theory, and experimental design at the undergraduate and graduate levels. He has not received external grant information in the provided text, but his research output suggests active engagement in funded or independent research projects. He leads methodological and applied research in sports analytics, often co-supervising students with colleagues across institutions. His lab or research group appears focused on developing and applying statistical tools for performance analysis and decision-making in sports, supported by computational implementations such as the R package ngr .

Ruben Loaiza Mayaمشاهده پروفایل

دانشیار

• Copula Modelling
• Bayesian Estimation Methods
• Time Series Analysis
• +۴ مورد دیگر

Ruben Loaiza-Maya is an Associate Professor (Research) in the Department of Econometrics and Business Statistics at Monash University. He holds a PhD in Econometrics from the University of Melbourne and an undergraduate degree in Economics from Universidad Nacional de Colombia (Medellin). His research focuses on Copula Modelling, Bayesian Estimation Methods, Time Series Analysis, and Macroeconomic/Financial Forecasting. Key contributions include advancements in variational inference, state space models, and robust forecasting techniques under model misspecification. He leads the active project 'Variational Inference for Intractable and Misspecified State Space Models' (2023–2026), funded as a Primary Chief Investigator. His work contributes to UN Sustainable Development Goals through methodological advancements in economic and financial analysis. Recent research emphasizes scalable Bayesian methods, hybrid variational approaches, and efficient computational techniques for high-dimensional models. Publications span prestigious journals like the International Journal of Forecasting, Journal of Econometrics, and Journal of Business and Economic Statistics. Notable collaborations include studies on copula-based time series forecasting and robust approximate Bayesian computation. His work bridges theoretical econometrics with practical applications in risk management and macroeconomic policy.

Jeffrey Sanford Russellمشاهده پروفایل

استاد

• Decision Theory
• Ethics
• Formal Epistemology
• +۴ مورد دیگر

Jeffrey Sanford Russell is a Professor of Philosophy at the University of Southern California's School of Philosophy, where he also serves as Director of Graduate Studies. He holds a PhD from New York University (2011). His research focuses on decision theory, ethics, formal epistemology, metaphysics, and the philosophy of religion and mathematics. Russell teaches courses on topics like probability, rational choice, and the limits of logic, emphasizing interdisciplinary approaches blending philosophy with mathematics, psychology, and economics. His work explores foundational questions in ethics, such as infinite value aggregation paradoxes, and decision theory, including stochastic dominance and self-locating evidence in cosmology. He has contributed to debates on multiverse hypotheses, moral hedging, and the structure of possible worlds. Russell’s publications appear in top journals like Noûs , Philosophical Review , and Philosophy and Phenomenological Research . His recent research addresses moral and epistemic challenges in infinite scenarios, such as infinite ethics and fanaticism in decision-making. He also investigates the intersection of metaphysics and formal logic, including composition principles and qualitative grounds for non-qualitative facts. Russell’s work bridges abstract philosophical inquiry with practical implications, such as ethical dilemmas in collective action and the epistemology of divine hiddenness.

• Bioinformatics
• Genomic Epidemiology
• Metagenomics
• +۳ مورد دیگر

Tommi Mäklin is a researcher affiliated with the University of Helsinki, conducting interdisciplinary research at the intersection of bioinformatics, microbiology, and genomic epidemiology. His work focuses on bacterial pathogen analysis, metagenomics, and the application of computational methods to study infectious diseases. Mäklin has contributed to high-impact studies on topics such as colibactin-producing Escherichia coli's link to cancer incidence, hospital-acquired infections during the pandemic, and enhanced metagenomic analysis tools like TRACS and Themisto. Key affiliations include visiting research positions at the University of Oslo (2024) and EMBL European Bioinformatics Institute (2023–2023), where he received the Theory@EMBL Visitor fellowship. His research has been featured in prominent journals like Nature Communications and The Lancet Microbe , with findings highlighted in media such as Yle and HS. Education: Not explicitly stated in provided materials. Research Interests: Genomic epidemiology, bacterial pathogen transmission, metagenomics, antibiotic resistance, and computational methods for microbial analysis. His recent work explores geographical cancer incidence correlations with bacterial exposure, hospital pathogen surveillance, and scalable genomic tools for outbreak analysis. Awards include recognition for methodological advancements in bioinformatics and microbial genomics.